(Use R) Consider the 'squid1.xlsx' dataset. These data were originally collected as part of a study published in Aquatic Living Resources in 2005. The aim of the study was to investigate the seasonal patterns of investment in somatic and reproductive tissues in the long finned squid Loligo forbesi caught in Scottish waters. Squid were caught monthly from December 1989 - July 1991 (month and year variables). After capture, each squid was given a unique specimen code, weighed (weight) and the sex determined (sex - only female squid are included here). The size of individuals was also measured as the dorsal mantle length (DML) and the mantle weight measured without internal organs (eviscerate.weight). The gonads were weighed (ovary.weight) along with the accessory reproductive organ (the nidamental gland, nid weight, nid length). Each individual was also assigned a categorical measure of maturity (maturity stage, ranging from 1 to 5 with 1 = immature, 5 = mature). The digestive gland weight (dig weight) was also recorded to assess nutritional status of the individual. Create dotplots for the following variables; DML, weight, nid length, and ovary weight. Do these variables contain any unusually large or small observations? Create histograms for the variables; DML, weight, eviscerate weight, and ovary weight. Create a single figure with all 4 plots. Export your plot as a pdf file. Plot the relationship between DML on the x-axis and weight on the y-axis. How would you describe this relationship? Is it linear? Save your plots as a png file. Create a boxplot to visualize the differences in DML at each maturity stage. Include x and y axes labels in your plot. Create a violin plot to visualize the differences in DML at each maturity stage. Use the coplot() function to plot the relationship between DML on the x-axis and square root transformed weight on the y-axis for each level of maturity stage. Does the relationship between DML and weight look different for each maturity stage. If you prefer, you can also create a similar plot using the xyplot() function from the lattice package. Create a pairs plot for the variables; DML, weight, eviscerate.weight, ovary.weight, nid length, and nid weight, Modify your pairs plot to include a histogram of the variables on the diagonal panel and include a correlation coefficient for each relationship on the upper triangle panels. Also include a smoother in the lower triangle panels to help visualize these relationships. 2 1002 1989 B 05128901 405128901 5 05128901 1003 1989 12 1005 1989 222 152 2 3 174 1 ample.no specimen year month weight sex maturity.stage DML eviscerate.weight dig.weight nid.length nid.weight ovary.weight 05128901 12 87.5 4.648 39.4 2.46 1.68 105.9 2 1 153 62.6 3.138 24.1 0.319 0.103 12 138.4 2 2 169 79.4 0.307 39 1.169 0.289 1007 1989 12 140.8 2 2 175 83.1 4.123 41.4 1.631 0.252 5 05128901 1008 1989 12 126.2 2 3 169 72.2 3.605 39.8 2.03 0.86 705128901 1009 1989 12 54.3 2 1 116 30.2 1.092 20 0.148 0.016 B 05128901 1011 1989 12 81.2 2 2 135 46.6 2.168 14 0.252 0.043 905128901 1013 1989 12 182.7 2 0 05128901 1014 1989 12 141.6 1 05128901 1017 1989 12 218 222 3 192 107.7 2.026 55 5.614 5.84 3 170 72.3 2.731 44 2.557 1.846 4 205 121.8 4.916 53 6.062 4.097 2 05128901 1020 1989 12 194.2 2 3 190 126.7 2.977 430.2 2.939 1.196 3 05128901 1023 1989 12 182.4 2 4 170 113.2 3.021 51 6.04 4.027 4 16019001 1003 1990 1 401 5 16019001 1005 1990 1 693 2, 4 257 187 16.05 80 18.858 21.072 2 5 323 347 22.241 100 28.727 47.79 6 19019001 1001 1990 1 354.6 7 00039001 1001 1990 3 311 8 00039001 1003 1990 3 297 9 00039001 1004 1990 3 339 0 00039001 1006 1990 3 258 1 00039001 1007 1990 3 397 2 00039001 1010 1990 3 504 3 02039001 1001 1990 3 132 4 02039001 1002 1990 3 224 5 02039001 1003 1990 3 331 6 02039002 1001 1990 3 220 7 02039002 1003 1990 3 301 8 02039002 1005 1990 3 367 9 04039001 1001 1990 3 249 -0 04039001 104039002 2 04039003 1002 1990 3 304 1001 1990 3 400 1002 1990 3 349 3 06039001 1001 1990 3 454 4 06039001 1003 1990 3 244 5 06039001 1004 1990 3 460 6 06039002 1001 1990 3 351 N22222222222222222222 4 246 171.7 12.6 84 18.3 19.4 4 240 169 10.223 70 9.831 12.881 4 225 142 5.931 85 16.298 21.15 5 234 167 7.93 83 17.176 25.55 4 209 126 3.784 80 13.391 12.816 5 250 198 11.075 83 15.044 21.37 4 272 221 13.044 96 35.42 29.39 4 159 60 3.36 53 5.421 7.59 5 205 100 5.93 76 10.073 16.3 5 250 162 9.71 93 15.499 23.52 5 205 99 5.293 74 12.712 14.76 5 223 146 7.674 76 16.046 21.92 5 233 169 9.23 87 23.003 32.77 4 216 114 3.768 70 11.656 21.365 5 223 147 6.871 96 19.79 21.077 4 255 206 9.94 89 20.593 30.042 5 250 163 7.074 90 19.264 27.284 4 264 213 14.547 90 26.35 29.95 5 215 111 5.453 71 13.752 15.3 5 255 220 13.46 102 31.795 36.89 5 244 179 6.776 87 18.604 24.109 -7 06039002 806039003 1002 1990 3 317 2 5 222 145 8.005 76 18.871 18.653 1009 1990 3 392 2 4 266 179 9.703 93 22.848 27.91 1001 1000 3 ค 200 307 105 ОЛ АС 00020001 squid1 +
(Use R) Consider the 'squid1.xlsx' dataset. These data were originally collected as part of a study published in Aquatic Living Resources in 2005. The aim of the study was to investigate the seasonal patterns of investment in somatic and reproductive tissues in the long finned squid Loligo forbesi caught in Scottish waters. Squid were caught monthly from December 1989 - July 1991 (month and year variables). After capture, each squid was given a unique specimen code, weighed (weight) and the sex determined (sex - only female squid are included here). The size of individuals was also measured as the dorsal mantle length (DML) and the mantle weight measured without internal organs (eviscerate.weight). The gonads were weighed (ovary.weight) along with the accessory reproductive organ (the nidamental gland, nid weight, nid length). Each individual was also assigned a categorical measure of maturity (maturity stage, ranging from 1 to 5 with 1 = immature, 5 = mature). The digestive gland weight (dig weight) was also recorded to assess nutritional status of the individual. Create dotplots for the following variables; DML, weight, nid length, and ovary weight. Do these variables contain any unusually large or small observations? Create histograms for the variables; DML, weight, eviscerate weight, and ovary weight. Create a single figure with all 4 plots. Export your plot as a pdf file. Plot the relationship between DML on the x-axis and weight on the y-axis. How would you describe this relationship? Is it linear? Save your plots as a png file. Create a boxplot to visualize the differences in DML at each maturity stage. Include x and y axes labels in your plot. Create a violin plot to visualize the differences in DML at each maturity stage. Use the coplot() function to plot the relationship between DML on the x-axis and square root transformed weight on the y-axis for each level of maturity stage. Does the relationship between DML and weight look different for each maturity stage. If you prefer, you can also create a similar plot using the xyplot() function from the lattice package. Create a pairs plot for the variables; DML, weight, eviscerate.weight, ovary.weight, nid length, and nid weight, Modify your pairs plot to include a histogram of the variables on the diagonal panel and include a correlation coefficient for each relationship on the upper triangle panels. Also include a smoother in the lower triangle panels to help visualize these relationships. 2 1002 1989 B 05128901 405128901 5 05128901 1003 1989 12 1005 1989 222 152 2 3 174 1 ample.no specimen year month weight sex maturity.stage DML eviscerate.weight dig.weight nid.length nid.weight ovary.weight 05128901 12 87.5 4.648 39.4 2.46 1.68 105.9 2 1 153 62.6 3.138 24.1 0.319 0.103 12 138.4 2 2 169 79.4 0.307 39 1.169 0.289 1007 1989 12 140.8 2 2 175 83.1 4.123 41.4 1.631 0.252 5 05128901 1008 1989 12 126.2 2 3 169 72.2 3.605 39.8 2.03 0.86 705128901 1009 1989 12 54.3 2 1 116 30.2 1.092 20 0.148 0.016 B 05128901 1011 1989 12 81.2 2 2 135 46.6 2.168 14 0.252 0.043 905128901 1013 1989 12 182.7 2 0 05128901 1014 1989 12 141.6 1 05128901 1017 1989 12 218 222 3 192 107.7 2.026 55 5.614 5.84 3 170 72.3 2.731 44 2.557 1.846 4 205 121.8 4.916 53 6.062 4.097 2 05128901 1020 1989 12 194.2 2 3 190 126.7 2.977 430.2 2.939 1.196 3 05128901 1023 1989 12 182.4 2 4 170 113.2 3.021 51 6.04 4.027 4 16019001 1003 1990 1 401 5 16019001 1005 1990 1 693 2, 4 257 187 16.05 80 18.858 21.072 2 5 323 347 22.241 100 28.727 47.79 6 19019001 1001 1990 1 354.6 7 00039001 1001 1990 3 311 8 00039001 1003 1990 3 297 9 00039001 1004 1990 3 339 0 00039001 1006 1990 3 258 1 00039001 1007 1990 3 397 2 00039001 1010 1990 3 504 3 02039001 1001 1990 3 132 4 02039001 1002 1990 3 224 5 02039001 1003 1990 3 331 6 02039002 1001 1990 3 220 7 02039002 1003 1990 3 301 8 02039002 1005 1990 3 367 9 04039001 1001 1990 3 249 -0 04039001 104039002 2 04039003 1002 1990 3 304 1001 1990 3 400 1002 1990 3 349 3 06039001 1001 1990 3 454 4 06039001 1003 1990 3 244 5 06039001 1004 1990 3 460 6 06039002 1001 1990 3 351 N22222222222222222222 4 246 171.7 12.6 84 18.3 19.4 4 240 169 10.223 70 9.831 12.881 4 225 142 5.931 85 16.298 21.15 5 234 167 7.93 83 17.176 25.55 4 209 126 3.784 80 13.391 12.816 5 250 198 11.075 83 15.044 21.37 4 272 221 13.044 96 35.42 29.39 4 159 60 3.36 53 5.421 7.59 5 205 100 5.93 76 10.073 16.3 5 250 162 9.71 93 15.499 23.52 5 205 99 5.293 74 12.712 14.76 5 223 146 7.674 76 16.046 21.92 5 233 169 9.23 87 23.003 32.77 4 216 114 3.768 70 11.656 21.365 5 223 147 6.871 96 19.79 21.077 4 255 206 9.94 89 20.593 30.042 5 250 163 7.074 90 19.264 27.284 4 264 213 14.547 90 26.35 29.95 5 215 111 5.453 71 13.752 15.3 5 255 220 13.46 102 31.795 36.89 5 244 179 6.776 87 18.604 24.109 -7 06039002 806039003 1002 1990 3 317 2 5 222 145 8.005 76 18.871 18.653 1009 1990 3 392 2 4 266 179 9.703 93 22.848 27.91 1001 1000 3 ค 200 307 105 ОЛ АС 00020001 squid1 +
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
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Chapter5: A Survey Of Other Common Functions
Section5.3: Modeling Data With Power Functions
Problem 6E: Urban Travel Times Population of cities and driving times are related, as shown in the accompanying...
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